Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 188-193· 0 citations· 18 references
Abstract
Magnetic resonance imaging (MRI) is a crucial component of the medical diagnostic and therapeutic approach for brain tumors, as early detection of anomalous tissue considerably enhances patient outcomes. MRI images may obscure significant tumor characteristics owing to noise, inadequate contrast, and erratic intensity distributions. This work examines the application of ResNet169, EfficientNetB0, and a hybrid fused model (ResNetEffi169B0) to address challenges in deep learning-based image improvement and categorization. The performance metrics included F1-score, Accuracy, Precision, Sensitivity, Entropy, SSIM, and MSE to test the models. The results show that EfficientNetB0 has the best PSNR (11.45), SSIM (0.308), and accuracy (0.601) when it comes to classification and improvement. The ResNet169 model's high sensitivity of 0.463 showed that it could dependably find tumor regions. The hybrid approach used the best features of both styles to create balanced results that made diagnoses more consistent and images clearer. The fusion method enhances the quality and structural data of magnetic resonance imaging (MRI) scan slices, leading to more precise classification and enhanced tumor visibility. This study examines the potential of hybrid deep learning models to enhance computer-aided diagnostic tools in medical imaging for improved brain tumor identification.
The study adds a rigorous benchmarking mechanism and empirical evidence for adopting ResNet50 as a robust model for multi-class brain tumour diagnosis and highlights the power of deep residual learning for solving some of the difficulties in classifying brain MRI, such as inter-class similarity and feature heterogeneity.
Prabha Kumaresan, Xin Tian Lim· International Journal on Rob...· 0 citations
The progress in medical imaging technology, including Magnetic Resonance Imaging (MRI), has greatly aided in the prompt identification and diagnosis of brain cancers. This study article provides a comprehensive examination of the use of machine learning methods to evaluate the seriousness of brain tumors using MRI images. The project seeks to assess the latest approaches, problems, and prospects in using machine learning algorithms to improve the precision and effectiveness of tumor severity diagnosis. The review involves a comprehensive examination of several machine learning techniques, encompassing both conventional methods and current breakthroughs like deep learning models, within the specific context of evaluating the severity of brain tumors. The study investigates the use of several strategies for extracting features, preprocessing data, and using classification algorithms to distinguish between benign and malignant tumors. In addition, the research examines the combination of many types of imaging data and the inclusion of clinical information to enhance the overall prediction accuracy. In addition, this study presents a thorough analysis of the current datasets, benchmarking methods, and assessment criteria to offer valuable insights into the dependability and applicability of the suggested models. The article discusses the difficulties related to having a restricted number of annotated datasets, the comprehensibility of intricate models, and the moral concerns when implementing machine learning in healthcare environments. The survey ends with a discussion on possible future research approaches, highlighting the need for joint endeavors among medical practitioners, image analysts, and machine learning specialists to create strong and practical models for clinical use. The results of this study are anticipated to aid in the progress of medical image analysis and promote the creation of more precise and dependable methods for identifying and describing brain cancers.
D. Rani, Anjaiah Adepu· 2026 4th International Confe...· 0 citations
This study investigates the application of deep learning architectures, including Convolutional Neural Network, VGG16, VGG19, ResNet50, and MobileNet, for brain tumor detection and classification and confirms that advanced deep learning architectures not only achieve high classification accuracy but also improve interpretability, thereby offering reliable and clinically applicable solutions for automated brain tumor diagnosis.
H. Uzel, Feyyaz Alpsalaz, Yıldırım Özüpak et al.· Computers and Electronics in...· 0 citations
Brain tumors affect millions of patients worldwide. Magnetic resonance imaging (MRI) is the preferred detection modality due to its radiation-free nature, high soft-tissue contrast, and three-dimensional (3D) imaging capabilities. However, the 3D complexity of MRI scans makes manual classification time-consuming, inefficient, and prone to errors. Consequently, developing high-precision automated classification is crucial in neurology. This study proposes BrainTumor CNN, a convolutional neural network (CNN) for classifying brain tumor MRI images. It leverages transfer learning via a pre-trained ResNet-18 network, integrating data augmentation and Dropout regularization to enhance robustness and generalization. The model demonstrates exceptional diagnostic performance, achieving 98.6% accuracy, with precision, recall, and F1-score all reaching 99.8%. By balancing high accuracy with low computational cost, this study provides an efficient diagnostic tool suitable for real-time clinical deployment.
Yicheng Xu· International Conference on...· 0 citations
Accurate classification of brain tumors using magnetic resonance imaging (MRI) is essential to clinical diagnosis and treatment. Nevertheless, the wide diversity in a single type of disease and high similarity between the tumors in different categories pose considerable challenges for deep learning models due to the characteristics of CNNs that are mainly for local features extracted, the necessity of reducing these limitations and constraints. This paper discusses an innovative hybrid deep learning paradigm in which an image is modeled by means of a vision transformer (ViT) and a Bi-directional long-term memory network (BiLSTM), resulting in an effective brain tumor classification. The application is based on the framework of ViT, capable of modeling overall context to extract high-level distinguishing features of MRI images, and the BiLSTM successfully capturing sequential dependencies inside of the extracted feature representations. This hybrid architecture is able to be very rich in modelling the spatial and contextual relationships that come with complex medical images. Results show that ViT–BiLSTM's classification performance is superior to those of traditional deep learning methods: among all the tumor categories its accuracy is higher, its fine-tuning more perfect, as well as, its Recall rates greater. This study demonstrates the efficacy of transformer-based hybrid architectures for medical image analysis with the proposal that by integrating a holistic attention framework with sequential modeling, they can yield substantially better patient diagnosing outcomes. The presented model is not only a viable recommendation for computer assisted diagnostic systems, but is also likely to help with clinical decision making in healthcare fields. The experimental results indicate that the proposed framework maintains a strong balance between accuracy and recall. Specifically, the model achieved accuracy/recall values of 92.5%/92.0% for gliomas, 91.2%/90.7% for meningiomas, and 93.5%/94.0% for brain tumors, resulting in high and consistent F1 scores across all categories.
Nagham Salim Mohammed, Omar S. Almolaa, A. S. Abdullah et al.· ITEGAM- Journal of Engineeri...· 0 citations
One of the most serious conditions affecting the nervous system is brain tumors, and a patient’s successful treatment depends on a correct diagnosis. A radiologist’s manual processing of MRI images takes a lot of time and can result in some incorrect diagnoses, particularly when the tumor’s boundaries are unclear. In order to address this issue, this paper suggests an automated system for brain tumor detection that combines deep learning algorithms with OpenCV-based image processing methods. First, the MRI pictures are preprocessed using methods for region of interest extraction, noise reduction, and contrast enhancement. A convolutional neural network is then fed the preprocessed images in order to identify and detect tumors. It is anticipated that the suggested system will increase tumor detection accuracy without sacrificing efficiency. The experimental results indicate that the combination of traditional image processing techniques with deep learning algorithms can improve the performance of the system and provide a useful tool for computer-aided medical diagnosis.
I. Harshini, M. Abhinaya, Dr.M.V.V.Siva Prasad· International Conference Com...· 0 citations